Method of double weighting parallel interference cancellation

ABSTRACT

A method for double weighting parallel interference cancellation in CDMA mobile communication system, integrates ideas of both partial weighting method and weighting method based on Bayes rule. When making a decision on each symbol of the user, calculating reliability coefficient of a decision result on the symbol of the user according to the weighting algorithm based on Bayes rule; weighting regenerated signal of the symbol of the user with the reliability coefficient. During process of MAI estimating and removing, obtaining the MAI on the expected user from weighted regenerated signal of other users in chip level; and then setting a weight value, weighting the MAI with the weight value. Finally, removing weighted MAI from received signal, which means partially removing MAI produced by other users on the expected user. At the same time, the present invention also discloses a double weighting PIC method with simplified algorithm, which transfers weighting in chip level to weighting in symbol level, replacing a Hyperbolic tangent decision with a piecewise linear decision method or a look-up table method.

FIELD OF THE TECHNOLOGY

The present invention generally relates to multiple user detection (MUD)technique in CDMA mobile communication system, and more particularly, toa method for double weighting parallel interference cancellation in CDMAsystem.

BACKGROUND OF THE INVENTION

Because of the advantages in great capacity, high service quality andexcellent secrecy property, CDMA mobile communication system has becometendency of 3G mobile communication developments. Wherein, multiple userdetection (MUD) is one kind of enhanced technique that can overcomelimitations on CDMA system capacity produced by multiple accessinterference (MAI), in order to improve capacity and performance of theCDMA system.

With help of the information of multi-users, MUD technique can jointlydetect signals from multi-users, in order to reduce influences of MAI onperformance of the receiver, and heighten capacity of the system as muchas possible. At present, MUD comprises maximum likelihood sequencedetector that is the best detector, linear multiple user detector andinterference cancellation multiple user detector that are second bestdetectors. Wherein, interference cancellation multiple user detectioncomprises the step of regarding signal from expected user as usefulsignal, removing and interference of other users from received signalsto obtain signal of expected user, and then detecting the signal of thesaid expected user. In this way, performance of the system can belargely raised.

The interference cancellation multiple user detection can be classifiedinto a serial interference cancellation (SIC) and a parallelinterference cancellation (PIC). SIC sorts the user signals in powerdescending order and cancels interference in serial, the method has abetter performance compared with single user detection but with longertime delay, and must sort signals of the users according to their power,which needs heavy calculation and is sensitive to the original signalsestimation. PIC removes interference of other users in parallel fromreceived signals, which has the advantages of short time delay andsimpler calculation.

Prior PIC methods comprise traditional PIC method, partial PIC methodand weighting PIC method that is based on Bayes rule.

Compared with single-user detection, the traditional PIC method canraise system performance in a large extent under high signal-to-noiseratio, but has a lower raise under low signal-to-noise ratio.

Being different from the traditional PIC method which removes MAIinfluence on expected users completely from received signals, thepartial PIC method sets a weight value for each stage of interferencecancellation to weight the MAI influence on the expected users, andpartially removes the MAI during the interfere cancellation process. Thetraditional PIC method within Gaussian channel removes all the MAI onthe expected users from the received signals, of course, the signalestimation on the expected users under this circumstance is biased; onthe other hand, partial PIC method merely removes the MAI partially,which can correct estimation biases on the expected users, makingdecision results more reliable. At circumstance of low signal-to-noiseratio, partial PIC method has obviously better performance than that ofthe traditional PIC method.

The patent U.S. Pat. No. 5,418,814 discloses the weighting PIC methodthat is based on Bayes rule. Although being a weighting method, itsweighting principle, which is a symbol-level weighting method that isbased on a minimum mean of decision cost, is different from that of thepartial PIC method. When creating a decision cost function, the methodtakes the minimum mean of decision cost as rule, to determinereliability coefficient of the decision result of each symbol, and tomake symbol level weighting with the coefficient on the signalsregenerated from the symbol. So only part of the interference producedby the symbol of the users is removed during the MAI eliminatingprocess. The method has better performance than that of the traditionalPIC method, especially at circumstance of low signal-to-noise ratio, andits improvement in performance is perfectly obvious.

Although both of the two methods above effectively improve performanceof traditional PIC method under low signal-to-noise ratio, the extent isvery limited.

SUMMARY OF THE INVENTION

It is an object to provide a method for double weighting parallelinterference cancellation in CDMA communication system, which improvesMUD performance without largely increasing its complexity, especiallysolves disadvantages of the prior art under circumstances with lowsignal-to-noise ratio.

A method for double weighting parallel interference cancellation in CDMAsystem according to the present invention comprises following steps of:

(a) Making a multi-path despreading, channel estimation and a multi-pathcombining on an input signal of a user by a RAKE receiver;

(b) Making a hard decision on a result of the multi-path combining;

(c) Calculating a reliability coefficient for a result of the harddecision of each symbol by the multi-path combining result and a valueof the channel estimation;

(d) Regenerating a weighted signal in chip level for the user by thehard decision result, the reliability coefficient and the channelestimation value;

(e) Estimating a multiple access interference on each user by theweighted regenerated signal in chip level inputted in parallel of eachuser, and setting a weight value for a interference cancellation indifferent stages, weighting the multiple access interference on aexpected user;

(f) Subtracting the weighted multiple access interference on theexpected user from a base band signal of a received signal, andobtaining an output signal of the said expected user, which is also aninput signal of the said expected user in the next stage of parallelinterference cancellation.

Combining the partial PIC and the weighting PIC based on Bayes rule,bringing forward double weighting PIC method, the method disclosedaccording to the present invention has advantages of not only weightingalgorithm based on Bayes rule, which has minimum decision cost in symbollevel, but also partial weighting algorithm, which can make up thestatistical signal estimation biases on users. What is more, the methodenormously improves gains at circumstance of low signal-to-noise ratiowithout large increases on calculation workload comparing with theweighting PIC based on Bayes rule, and achieves a large improvement onperformance of the system according to the present invention whencomparing with partial weighting and weighting methods based on Bayesrule.

It is also a further object to provide a simplified double weighting PICmethod, in order to reduce complexity of the algorithm while keepingperformance of said double weighting PIC method unchanged.

A further object of the invention is to provide a double weighting PICmethod with a simplified algorithm, which reduces complexity of thealgorithm while keeping performance of said double weighting PIC methodunchanged.

A double weighting PIC method with a simplified algorithm according tothe present invention comprises following steps:

(a) Making a multi-path despreading, channel estimation and a multi-pathcombining on an input signal of a user by a RAKE receiver;

(b) Making a soft decision on a result of the multi-path combining and avalue of the channel estimation;

(c) Setting a weight value for the soft decision in different stages,and weighting the soft decision in symbol level;

(d) Regenerating a weighted signal of the user in chip level by theweighted soft decision result and the channel estimation value;

(e) Estimating multiple access interference on each user by the weightedregenerated signal in chip level inputted in parallel of each user;

(f) Subtracting the multiple access interference on an expected userfrom a base band signal of a received signal, and obtaining an outputsignal of the expected user, which is also an input signal of the saidexpected user in the next stage of parallel interference cancellation.

The double weighting PIC method with the simplified algorithm accordingto the present invention, changing a chip level weighting into symbollevel weighting, which can reduce complexity of the algorithm whilekeeping performance of said double weighting PIC method unchanged.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a schematic block diagram of multiple stages architecture indouble weighting PIC receiver.

FIG. 2 is a schematic block diagram of PIC structure in double weightingPIC method.

FIG. 3 is a schematic block diagram of PIC structure of the last stage.

FIG. 4 is a schematic block diagram of PIC structure in simplifieddouble weighting PIC method.

EMBODIMENTS OF THE INVENTION

The present invention will be described in more detail hereinafter withreference to the accompanying drawings and embodiments.

The method of the present invention integrates both partial weightingand the weighting based on Bayes rule. When decisions on each symbol ofthe user are made, a reliability coefficient of decision result of thesymbol is calculated according to the weighting algorithm formula basedon Bayes rule, and then regenerated signal of the symbol of the user isweighted with the reliability coefficient. During the MAI estimating andremoving processes, the MAI on expected users produced by other users isgot by the weighted regenerated signal of other users in chip level;after a weight value is set, the obtained MAI is weighted with theweight value. Finally, the weighted MAI is subtracted from receivedsignals, which means partially removing the MAI on expected usersproduced by other users. Now above method is applied in each stage ofPIC architecture.

As shown in FIG. 1, multistage structure in double weighting PIC methodis the same with that of traditional PIC method. The first stage PICstructure 1 takes base band signals r(t) of the received signal as inputof each user for processing, and the obtained output of each user areinput of each user in PIC structure of the next stage; after processingon input of each user in the second stage PIC structure, the obtainedoutput become input of each user in PIC structure of the next stage;stage by stage in this way, after processing on input of each user inPIC structure 2 of the last stage, the obtained output from each userhere will be the final result of the multistage PIC structure.

As shown in FIG. 2, PIC structure in double weighting PIC method is thesame with that of the weighting PIC method based on Bayes rule. PICstructure of the last stage of the method is same as that of thetraditional PIC method, which is shown in FIG. 3.

Now refer to FIG. 1, which shows base band signals r(t) of the receivedsignals enter the first stage PIC structure 1 in parallel mode. As shownin FIG. 2, input signal entering into PIC structure in parallel istransmitted to RAKE receiver 3 of each user respectively. RAKE receiver3 despreads the input signal first, then makes channel estimationaccording to the result of despreading, and makes multi-path combiningat last, then transmits the multi-path combining result to hard decisionmaker 4 and decision reliability calculator 7 simultaneously, and sendsresult of the channel estimation to decision reliability calculator 7and signal regenerator 5 simultaneously. Hard decision maker 4 makeshard decision for the input, and transmits the result to signalregenerator 5.

In fading channel environment, base band signals r(t) of the receivedsignal can be expressed as:

$\begin{matrix}{{r(t)} = {{{\sum\limits_{i = 1}^{N}\;{\sum\limits_{l = 1}^{L}\;{a_{il}{S_{i}\left( {t - \tau_{il}} \right)}}}} + {Z(t)}} = {{\sum\limits_{i = 1}^{N}\;{\sum\limits_{l = 1}^{L}\;{a_{il}\sqrt{P_{i}}{b_{i}\left( {t - \tau_{il}} \right)}{c_{i}\left( {t - \tau_{il}} \right)}}}} + {Z(t)}}}} & (1)\end{matrix}$

Where, r(t) refers to the base band signals of the received signals;a_(il) refers to a channel fading value of the ith user in the lth path,L is a path number; τ_(il) refers to a delay of the ith user in the lthpath; S_(i)(t) refers to a outbound signal of user i, N refers to totalusers number; P_(i) refers to a power of user i; b_(i)(t) refers to asymbol flow of user

$i,{{b_{i}(t)} = {\sum\limits_{m = {- \infty}}^{\infty}\;{a_{i}^{(m)}{p\left( {t - {mT}_{b}} \right)}}}},a_{i}^{(m)}$refers to the mth symbol of the ith user, p(t) refers to a signal pulsewhose cycle is T_(b), when there is no hindrance on the deducing processwith the algorithm, set p(t) as rectangular pulse (when t∈[0, T_(b)],p(t)=1; when t∉[0, T_(b)], p(t)=0); c_(i)(t) refers to a spread-spectrumcode of user i; Z(t) refers to channel noise.

In the kth stage PIC structure, input of RAKE receiver 3 of user i is

r_(i)^((k))(t).When

k = 1, r_(i)⁽¹⁾(t) = r(t).

After the RAKE receiver makes multi-path despreading on r_(i) ^((k))(t),and channel estimation according to the despreading result, multi-pathcombining is implemented. The lth path despreading result made by RAKEreceiver of user i is:

$\begin{matrix}{{{y_{i}^{{(m)}{(k)}}(l)} = {\frac{1}{\sqrt{T_{b}}}{\int_{{{({m - 1})}T_{b}} + \tau_{il}}^{{mT}_{b} + \tau_{il}}{{\partial{r_{i}^{(k)}(t)}}\;{c_{i}^{*}\left( {t - \tau_{il}} \right)}\ {\mathbb{d}t}}}}}\;} & (2)\end{matrix}$

Where, l=1, . . . , L.

With the maximal ratio combining, the multi-path combining result madeby RAKE receiver will be:

$\begin{matrix}{{Y_{i}^{{(m)}{(k)}} = {{Re}\left\{ y_{i}^{{(m)}{(k)}} \right\}}}\mspace{14mu}} & (3) \\{{Where},{y_{i}^{{(m)}{(k)}} = {\sum\limits_{l = 1}^{L}\;{A_{il}^{*}{y_{i}^{{(m)}{(k)}}(l)}}}}} & (4)\end{matrix}$

A_(il) is an estimation value of a_(il)√{square root over (P_(i))},a_(il) is a channel fading value in the lth path of the ith user, P_(i)refers to a power of user i.

The above multi-path combining results of the RAKE receiver can beexpressed as

Y_(i)^((m)(k)) = μ_(i)a_(i)^((m)) + n_(i),wherein, n_(i) is Gaussian White Noise, which obeys normal distributionN(0, σ_(i) ²), σ² is a noise power of

n_(i); a_(i)^((m))refers to the mth symbol of user i, whose value is +1 or −1, μ_(i) is areal number relating to channel fading.

The decision result for the mth symbol of the ith user is:

$\begin{matrix}{{\hat{a}}_{i}^{{(m)}{(k)}} = {{sgn}\left\{ Y_{i}^{{(m)}{(k)}} \right\}}} & (5)\end{matrix}$

The decision reliability calculator calculates the reliabilitycoefficient of the decision result made by hard decision maker throughtwo input signals, and transmits the reliability coefficient to signalregenerator 5. The reliability coefficient calculating formula of â_(i)^((m)(k)) is expressed as:

$\begin{matrix}{f_{i}^{{(m)}{(k)}} = {\tanh\left\{ {w\frac{\mu_{i}{Y_{i}^{{(m)}{(k)}}}}{\sigma_{i}^{2}}} \right\}}} & (6)\end{matrix}$

Where, w is a positive real number, which is used to make up errors innoise power estimation. The w values in different signal-to-noise ratioscan be determined via experiments, wherein w=1 at higher signal-to-noiseratios.

After obtaining regenerated signals of the user from three inputsignals, the signal regenerator transmits the regenerated signals todevice 6 for MAI estimation and partial interference cancellation.Weighted regenerated signal in chip level of user i is:

$\begin{matrix}{{g_{i}^{(k)}(t)} = {\sum\limits_{l = 1}^{L}\;{A_{il}{\sum\limits_{n = {- \infty}}^{\infty}\;{f_{i}^{{(n)}{(k)}}{\hat{a}}_{i}^{{(n)}{(k)}}{p\left( {t - {nT}_{b} - \tau_{il}} \right)}{c_{i}\left( {t - \tau_{il}} \right)}}}}}} & (7)\end{matrix}$

Where, p(t) is a signal pulse whose cycle is T_(b).

As shown in FIG. 2, the base band signals r(t) of the received signalsare also transmitted into MAI estimation and partial interferencecancellation device 6, which estimates MAI on each user according to theregenerated signals of each user inputted in parallel; the resultedsignal after removing MAI on certain user from the base band signalsr(t) of the received signals will be the output of the said user in thepresent stage PIC structure and the input of the said user in next stageof PIC structure.

In the kth stage PIC structure, MAI estimation of user i will be:

$\begin{matrix}{{\hat{I}}_{i}^{(k)} = {\sum\limits_{{j = 1},{j \neq i}}^{N}\;{g_{j}^{(k)}(t)}}} & (8)\end{matrix}$

Suppose weight value of the kth stage PIC structure is p^((k)); the MAIfrom formula (8) can be weighted and then interference cancellation ismade to obtain output r_(i) ^((k+1))(t) of user i in the kth stage PICstructure according to formula (9). r_(i) ^((k+1))(t) is the input ofRAKE receiver of user i in PIC structure of the next stage.

$\begin{matrix}{{r_{i}^{({k + 1})}(t)} = {{r(t)} - {p^{(k)}{\hat{I}}_{i}^{(k)}}}} & (9)\end{matrix}$

Different weight value p^((k)) can be set up for interferencecancellations in different stages, and preferably set p⁽¹⁾<p⁽²⁾ . . .<p^((k)) . . . <p^((S)), wherein k is the kth stage of the interferencecancellation, S refers to a stage number of the interferencecancellation.

Same process will be done for parallel input signals in PIC structure ofthe next stage. The process goes in this way stage by stage, and when itgoes PIC structure of the last stage, the parallel input signals will betransmitted respectively into RAKE receivers 3 of each user. Aftermulti-path dispreading, channel estimation and multi-path combining onthe input signal made by RAKE receiver of the user, soft output of theuser can be got. Said soft output of each user will be final result ofeach user in multistage PIC structure. In other words, multi-pathdispreading process according to formula (2), and multi-path combiningaccording to formula (3), (4) are made on the input signal of user i.And the soft output of user i obtained through multi-path combining willbe final result of user i in the multistage PIC structure. In thereceiver, soft output of the user will be transmitted to its decoder fordecoding.

Integrating the partial PIC and the weighting PIC based on Bayes rule,the present invention provides the double weighting PIC method, whichhas advantages of not only the weighting algorithm based on Bayes rule,which has advantages of Minimum decision Cost in symbol level, but alsoadvantages of the partial weighting algorithm, which can make up thestatistical signal estimation biases on users. At the same time, themethod has better performance than that of weighting PIC based on Bayesrule, which can improve gains at circumstance of signal-to-noise ratiowithout obvious increases in calculation workload and provide a largelyimproved performance compared with both partial weighting and weightingbased on Bayes rule.

Through analysis on calculation formula of said double weighting PICmethod, it is noticed that, formula (6) comprises the Hyperbolic tangentoperation, which is hard to implemented with prior hardware, and needsto develop new arithmetic chips, so the cost is much higher; moreover,

p^((k))Î_(i)^((k))in formula (9) is chip level multiplication with heavy calculationworkload.

In order to solve above disadvantages, the present invention furtherprovides a double weighting PIC method with simplified algorithm, whosemultistage architecture is shown in FIG. 1, PIC structure of thesimplified algorithm is illustrated in FIG. 4, PIC structure of thesimplified algorithm in the last stage is shown in FIG. 3.

Here is one of embodiments with simplified algorithm according to thepresent invention:

As shown in FIG. 1, base band signals r(t) of the received signals enterin parallel to the first stage PIC structure 1 in FIG. 1. Referring toFIG. 4, the input signals r(t) entered the PIC structure are transmittedinto RAKE receiver 3 of each user respectively. RAKE receiver 3implements channel estimation after dispreading the input signal, andcompletes multi-path combining at least. RAKE receiver 3 transmits themulti-path combining result to soft decision maker 8, and then at thesame time sends the channel estimation result to soft decision maker 8and signal regenerator 5.

In the kth stage PIC structure, multi-path combining result of user ican be presented by:

$\begin{matrix}{Y_{i}^{{(m)}{(k)}} = {{\mu_{i}a_{i}^{(m)}} + n_{i}}} & (10)\end{matrix}$

Where, n_(i) is Gaussian White Noise, which is subject to normaldistribution

N(0, σ_(i)²); a_(i)^((m))is the mth symbol of user i, whose value is +1 or −1, μ_(i) is a realnumber relating to channel fading.

Soft decision maker 8 makes soft decision on the input signal, the softdecision on multi-path combining result in RAKE receiver of user i is

ξ_(i)^((m)(k)) = f_(i)^((m)(k))â_(i)^((m)(k)),and

f_(i)^((m)(k))â_(i)^((m)(k))satisfies:

$\begin{matrix}{{f_{i}^{{(m)}{(k)}}{\hat{a}}_{i}^{{(m)}{(k)}}} = {\tanh\left\{ {w\frac{\mu_{i}Y_{i}^{{(m)}{(k)}}}{\sigma_{i^{2}}}} \right\}}} & (11)\end{matrix}$

Where,

â_(i)^((m)(k))is the decision result on the mth symbol of user i,

â_(i)^((m)(k)) = sgn{Y_(i)^((m)(k))}, f_(i)^((m)(k))is reliability coefficient of

â_(i)^((m)(k)).

Replacing the Hyberbolic tangent decision of said double weighting PICmethod with a piecewise linear decision, setting a piecewise lineardecision function as L(x), the method of replacing the Hyperbolictangent decision with the piecewise linear decision is to apply thepiecewise linear decision function L(x) to approach the Hyperbolictangent function tanh(x). Deducting process of the piecewise lineardecision function is as follows.

{circle around (1)}. Define the piecewise linear decision function L(x)

Because the Hyperbolic tangent function is an odd function:tanh(−x)=−tanh(x);, defineL(−x)=−L(x).

{circle around (2)}. Determine a threshold value θ

The Hyperbolic tangent function has following characteristics: when x→∞,tanh(x)→1; Therefore, set the threshold value θ>0, when x>θ, set L(x)=1;

{circle around (3)}. Determine a linear parameter Q

When 0≦x≦θ, equally divide field [0, θ] into Q small intervals, the qthsmall interval is

$\left\lbrack {x_{q - 1},x_{q}} \right\rbrack,{x_{q} = \frac{q\;\theta}{Q}},{x_{0} = 0},{{x_{Q} = \theta};}$

{circle around (4)}. The expression of L(x) within the qth smallinterval is as following.

In the interval [x_(q−1), x_(q)], define L(x) as a line segmentconnecting between point C_(q) and D_(q). Wherein, coordinate of pointC_(q) is Cq=(x_(q−1), tanh(x_(q−1))), coordinate of point D_(q) isDq=(x_(q), tanh(x_(q))). And approximate the tanh(x) curve within theinterval [x_(q−1), x_(q)] with said line segment C_(q)D_(q). Theequation of the line segment C_(q)D_(q) is:

$\begin{matrix}{{L_{q}(x)} = {{\tanh\left( x_{q - 1} \right)} + {\frac{{\tanh\left( x_{q} \right)} - {\tanh\left( x_{q - 1} \right)}}{x_{q} - x_{q - 1}}\left( {x - x_{q - 1}} \right)}}} & (12)\end{matrix}$

{circle around (5)}. With L(−x)=−L(x), the expression of L(x) in theinterval [−x_(q), −x_(q−)] is:L(x)=−L _(q)(−x)

{circle around (6)}. The expression of the piecewise linear decisionfunction L(x) is:

$\begin{matrix}{{L(x)} = \left\{ \begin{matrix}{1,{x > \theta}} \\{{L_{q}(x)},{x \in \left\lbrack {x_{q - 1},x_{q}} \right\rbrack}} \\{{- {L_{q}\left( {- x} \right)}},{x \in \left\lbrack {{- x_{q}},{- x_{q - 1}}} \right\rbrack}} \\{{- 1},{x < {- \theta}}}\end{matrix} \right.} & (13)\end{matrix}$

Soft decision maker 8 transmits the soft decision result to softdecision weighting device 9, which weights the soft decision result withformula (14), and sends the weighted result to signal regenerator 5. Theweighting operation is symbol level.

$\begin{matrix}{\rho_{i}^{{(m)}{(k)}} = {\xi_{i}^{{(m)}{(k)}} \cdot p^{(k)}}} & (14)\end{matrix}$

Signal regenerator 5 obtains the regenerated signal from two inputsignals according to following formula, and transmits the regeneratedsignal to MAI estimation and partial interference cancellation device 6,the weighted regenerated signal in chip level of user i can be expressedwith following formula:

$\begin{matrix}{{g_{i}^{(k)}(t)} = {\sum\limits_{l = 1}^{L}\;{A_{il}{\sum\limits_{n = {- \infty}}^{\infty}\;{\rho_{i}^{{(n)}{(k)}}{\hat{a}}_{i}^{{(n)}{(k)}}{p\left( {t - {nT}_{b} - \tau_{il}} \right)}{c_{i}\left( {t - \tau_{il}} \right)}}}}}} & (15)\end{matrix}$

As shown in FIG. 4, base band signals r(t) of the received signals arealso transmitted to MAI estimation and partial interference cancellationdevice 6, which estimates MAI on each user through the regeneratedsignal of each user inputted in parallel, in the kth stage PICalgorithm, estimation of MAI on user i is:

$\begin{matrix}{I_{i}^{(k)} = {\sum\limits_{{j = 1},{j \neq i}}^{N}\;{g_{j}^{(k)}(t)}}} & (16)\end{matrix}$

After MAI on user i is calculated, the method removes MAI on user i frombase band signals r(t) of the received signals, and implementsinterference cancellation on MAI according to formula (17):

$\begin{matrix}{{r_{i}^{({k + 1})}(t)} = {{r(t)} - {\hat{I}}_{i}^{(k)}}} & (17)\end{matrix}$

r_(i)^((k + 1))(t)is the output signal of user i in the kth stage PIC structure, and theinput signal into RAKE receiver of user i in PIC structure of the nextstage.

The signal that is obtained after removing MAI on the user from baseband signals r(t) of the received signals will become output signal ofthe said user in the present stage PIC structure, as well as inputsignal of the said user in next stage PIC structure. In the next stageof PIC structure, the parallel input signals will be processed in thesame way. The operation is carried out stage by stage in this way; whenbeing processed in PIC structure of the last stage, as shown in FIG. 3,the parallel input signals are transmitted into RAKE receiver 3 of eachuser separately, which implements despreading, channel estimation andmulti-path combining on the input signal. The soft output from each userhere will be the final result of the multistage PIC structure. In thereceiver, the soft output of the user is transmitted to decoder of thesaid user for decoding.

Another embodiment of simplified algorithm according to the presentinvention is as following.

Now refer to FIG. 1. The base band signals r(t) of the received signalsare transmitted into the first stage PIC structure 1 in parallel. Asshown in FIG. 4, said input signals r(t) are respectively transmittedinto RAKE receiver 3 of each user, which despreads the input signalfirst and makes channel estimation, and last implements multi-pathcombining. RAKE receiver 3 transmits the multi-path combining result tosoft decision maker 8, and sends channel estimation result to softdecision maker 8 and signal regenerator 5 at the same time. In the kthstage PIC structure, the multi-path combining result of user i can beexpressed as:

$\begin{matrix}{Y_{i}^{{(m)}{(k)}} = {{\mu_{i}a_{i}^{(m)}} + n_{i}}} & (18)\end{matrix}$

n_(i) is Gaussian White Noise, which is subject to normal distribution

N(0, σ_(i)²); a_(i)^((m))is the mth symbol of user i, whose value is +1 or −1. μ_(i) is a realnumber that is related with channel fading.

Soft decision maker 8 makes soft decision on the input signal, the softdecision on the multi-path combining result of RAKE receiver of user iis

ξ_(i)^((m)(k)) = f_(i)^((m)(k))â_(i)^((m)(k)),where

f_(i)^((m)(k))â_(i)^((m)(k))satisfies following formula:

$\begin{matrix}{{f_{i}^{{(m)}{(k)}}{\hat{a}}_{i}^{{(m)}{(k)}}} = {\tanh\left\{ {w\frac{\mu_{i}Y_{i}^{{(m)}{(k)}}}{\sigma_{i}^{2}}} \right\}}} & (19)\end{matrix}$

Where,

â_(i)^((m)(k))is a decision result of the mth symbol of user i,

â_(i)^((m)(k)) = sgn{Y_(i)^((m)(k))}, f_(i)^((m)(k))is a reliability coefficient of

â_(i)^((m)(k)).

When replacing the Hyperbolic tangent decision in the double weightingPIC method with a look-up table method, setting decision function of thelook-up table method as T(x), the method of replacing the Hyperbolictangent decision with the look-up table method is to approach theHyperbolic tangent function tanh(x) with the decision function T(x) ofthe look-up table method, whose deducing process is as follows.

{circle around (1)}. Define decision function T(x) of the look-up tablemethod

Because the Hyperbolic tangent function is an odd function:tanh(−x)=−tanh(x), defineT(−x)=−T(x);

{circle around (2)}. Determine a threshold value θ

Because the Hyperbolic tangent function has the characteristics of: whenx→∞, tanh(x)→1; set the threshold value θ>0 according to the presentinvention, when x>θ, set T(x)=1;

{circle around (3)}. Determine a linear parameter Q

When 0≦x≦θ, divide the field [0, θ] into Q small intervals equally,where the qth small interval is

$\left\lbrack {x_{q - 1},x_{q}} \right\rbrack,{x_{q} = \frac{q\;\theta}{Q}},{x_{0} = 0},{{x_{Q} = \theta};}$

{circle around (4)}. Expression of the T(x) in small interval q is asfollowing.

In the small interval [x_(q−1), x_(q)], set midpoint of the smallinterval as

$\frac{x_{q - 1} + x_{q}}{2},$and define T(x) as follows:

$\begin{matrix}{{T(x)} = {\tanh\left( \frac{x_{q - 1} + x_{q}}{2} \right)}} & (20)\end{matrix}$

{circle around (5)}. By means of T(−x)=−T(x), expression of the T(x) inthe interval [−θ, 0] can be obtained.

{circle around (6)}. Expression of the decision function T(x) in look-uptable method is:

$\begin{matrix}{{1,{x > \theta}}{{T(x)} = \left\{ {{\begin{matrix}{{\tanh\left( \frac{x_{q - 1} + x_{q}}{2} \right)},{x \in \left\lbrack {x_{q - 1},x_{q}} \right\rbrack}} \\{{- {\tanh\left( \frac{x_{q - 1} + x_{q}}{2} \right)}},{x \in \left\lbrack {{- x_{q}},{- x_{q - 1}}} \right\rbrack}}\end{matrix} - 1},{x < {- \theta}}} \right.}} & (21)\end{matrix}$

Soft decision maker 8 transmits the soft decision result to softdecision weighting device 9, which weights the soft decision result insymbol level with formula (22), and sends the weighted result to signalregenerator 5.

$\begin{matrix}{\rho_{i}^{{(m)}{(k)}} = {\xi_{i}^{{(m)}{(k)}} \cdot p^{(k)}}} & (22)\end{matrix}$

According to the formula below, signal regenerator 5 obtains theregenerated signal from two input signals, and transmits the regeneratedsignals to MAI estimation and partial interference cancellation device6; the weighted regenerated signal of user i in chip level can beexpressed as:

$\begin{matrix}{{g_{i}^{(k)}(t)} = {\sum\limits_{l = 1}^{L}\;{A_{il}{\sum\limits_{n = {- \infty}}^{\infty}\;{\rho_{i}^{{(n)}{(k)}}{p\left( {t - {nT}_{b} - \tau_{il}} \right)}{c_{i}\left( {t - \tau_{il}} \right)}}}}}} & (23)\end{matrix}$

As shown in FIG. 4, base band signals r(t) of the received signals arealso transmitted into MAI estimation and partial interferencecancellation device 6, which estimates MAI on each user based on theregenerated signal inputted in parallel of each user, in the kth stagePIC algorithm, the estimations of MAI on user i is:

$\begin{matrix}{{\hat{I}}_{i}^{(k)} = {\sum\limits_{{j = 1},{j \neq i}}^{N}\;{g_{j}^{(k)}(t)}}} & (24)\end{matrix}$

After MAI on user i is calculated, the method removes MAI on user i frombase band signals r(t) of the received signals, and implementsinterference cancellation on MAI according to formula (25):

$\begin{matrix}{{r_{i}^{({k + 1})}(t)} = {{r(t)} - {\hat{I}}_{i}^{(k)}}} & (25)\end{matrix}$

r_(i)^((k + 1))(t)is the output signal of user i in the kth stage PIC structure, and aswell as input signal of RAKE receiver of user i in the next stage PICstructure.

The signal that is obtained after removing MAI on the user from baseband signals r(t) of the received signals will become the output signalof the said user in the present stage PIC structure, as well as theinput signal of the said user in next stage of PIC structure. In PICstructure of the next stage, the parallel input signals will beprocessed in the same way. The operation is carried out stage by stagein this way; when processed in PIC structure of the last stage, as shownin FIG. 3, the parallel input signals are transmitted into RAKE receiver3 of each user separately, which implements despreading, channelestimation and multi-path combining on the input signal. The soft outputfrom each user here will be the final result of the multistage PICstructure. In the receiver, the soft output of the user is transmittedto its decoder for decoding.

1. A double weighting parallel interference cancellation method that canbe used in a CDMA mobile communication system, comprises: (a) Making amulti-path despreading, channel estimation and a multi-path combining onan input signal of a user by a RAKE receiver; (b) Making a hard decisionon a result of the multi-path combining; (c) Calculating a reliabilitycoefficient for a result of the hard decision of each symbol by themulti-path combining result and a value of the channel estimation; (d)Regenerating a weighted signal in chip level for the user by the harddecision result, the reliability coefficient and the channel estimationvalue; (e) Estimating a multiple access interference on each user by theweighted regenerated signal in chip level inputted in parallel of eachuser, and setting a weight value for a interference cancellation indifferent stages, weighting the multiple access interference on aexpected user; (f) Subtracting the weighted multiple access interferenceon the expected user from a base band signal of a received signal, andobtaining an output signal of the said expected user, which is also aninput signal of the said expected user in the next stage parallelinterference cancellation.
 2. The double weighting parallel interferencecancellation method according to claim 1, comprises, if the said inputsignal of user i in the kth stage parallel interference cancellation isexpressed as r_(i) ^((k))(t), In step (a), calculating the saidmulti-path combining result of user i by a formulaY_(i)^((m)(k)) = Re{y_(i)^((m)(k))},  which can be expressed asY_(i)^((m)(k)) = μ_(i)a_(i)^((m)) + n_(i),  where, n_(i) is a GaussianWhite Noise, which is subject to a normal distribution ofN(0, σ_(i)²), σ²  is a noise power of n_(i), a_(i)^((m))  is the mthsymbol of user i, whose value is +1 or −1, μ_(i) is a real number thatis related with channel fading; In step (b), calculating the said harddecision result of the mth symbol of user i by a formulaâ_(i)^((m)(k)) = sgn(Y_(i)^((m)(k))); In step (c), calculating the saidreliability coefficient of the said decision result on the mth symbol ofuser i by${f_{i}^{{(m)}{(k)}} = {\tanh\left\{ {w\frac{\mu_{i}{Y_{i}^{{(m)}{(k)}}}}{\sigma_{i}^{2}}} \right\}}},$ where, w is a positive real number; In step (d), calculating the saidweighted regenerated signal of user i in chip level by a formula${{g_{i}^{(k)}(t)} = {\sum\limits_{l = 1}^{L}\;{A_{il}{\sum\limits_{n = {- \infty}}^{\infty}\;{f_{i}^{{(n)}{(k)}}{\hat{a}}_{i}^{{(n)}{(k)}}{p\left( {t - {nT}_{b} - \tau_{il}} \right)}{c_{i}\left( {t - \tau_{il}} \right)}}}}}},$ where, A_(il) is a estimation value of a_(il)√{square root over(P_(i))}, a_(il) refers to a channel fading value of user i in the pathl, p_(i) is a power of user i; In step (e), calculating the saidmultiple access interference estimation of user i by a formula${I_{i}^{(k)} = {\sum\limits_{{j = 1},{j \neq i}}^{N}\;{g_{j}^{(k)}(t)}}};$Assuming the said weight value of the kth stage parallel interferencecancellation is p^((k)), then calculating the said output signal of useri in step (f) by a formular_(i)^((k + 1))(t) = r(t) − p^((k))Î_(i)^((k)),  where, r(t) is the saidbase band signal of the said received signal.
 3. The double weightingparallel interference cancellation method according to claim 2, whereinstep (c) of calculating the said reliability coefficient of the saiddecision result on the mth symbol of user i comprises, replacing thesaid Hyperbolic tangent decision with a piecewise linear decision, whichmeans approaching a Hyperbolic tangent function tanh(x) with a piecewiselinear decision function L(x), a expression of the piecewise lineardecision function L(x) is: 1, x > θ ${L(x)} = \left\{ {{\begin{matrix}\begin{matrix}{{L_{q}(x)},{x \in \left\lbrack {x_{q - 1},x_{q}} \right\rbrack}} \\{{- {L_{q}\left( {- x} \right)}},{x \in \left\lbrack {{- x_{q}},{- x_{q - 1}}} \right\rbrack}}\end{matrix}\end{matrix}.{- 1}},{x < {- \theta}}} \right.$
 4. The double weightingparallel interference cancellation method according to claim 2, whereinstep (c) of calculating the said reliability coefficient of the saiddecision result on the mth symbol of user i comprises, replacing thesaid Hyperbolic tangent decision with a look-up table method, whichmeans approaching a Hyperbolic tangent function tanh(x) with a decisionfunction T(x) of the look-up table method, a expression of the saiddecision function T(x) of the look-up table method is:$1,{{x > {\theta{T(x)}}} = \left\{ {{\begin{matrix}\begin{matrix}{{\tanh\left( \frac{x_{q - 1} + x_{q}}{2} \right)},{x \in \left\lbrack {x_{q - 1},x_{q}} \right\rbrack}} \\{{- {\tanh\left( \frac{x_{q - 1} + x_{q}}{2} \right)}},{x \in \left\lbrack {{- x_{q}},{- x_{q - 1}}} \right\rbrack}}\end{matrix}\end{matrix}.{- 1}},{x < {- \theta}}} \right.}$
 5. The double weightingparallel interference cancellation method according to claim 1, whereinstep (e) comprises, setting the different weight value p^((k)) for theinterference cancellation in different stages, wherein p⁽¹⁾<p⁽²⁾ . . .<p^((k)) . . . <p^((S)), where, k is the kth stage of the interferencecancellation, and S is a stage number of the interference cancellation.6. A double weighting parallel interference cancellation method that canbe used in CDMA mobile communication system, comprises: (a) Making amulti-path despreading, channel estimation and a multi-path combining onan input signal of a user by a RAKE receiver; (b) Making a soft decisionon a result of the multi-path combining and a value of the channelestimation; (c) Setting a weight value for the soft decision indifferent stages, and weighting the soft decision in symbol level; (d)Regenerating a weighted signal of the user in chip level by the weightedsoft decision result and the channel estimation value; (e) Estimatingmultiple access interference on each user by the weighted regeneratedsignal in chip level inputted in parallel of each user; (f) Subtractingthe multiple access interference on an expected user from a base bandsignal of a received signal, and obtaining an output signal of theexpected user, which is also an input signal of the said expected userin the next stage of parallel interference cancellation.
 7. The doubleweighting parallel interference cancellation method according to claim6, comprises, if the said input signal of user i in the kth stageparallel interference cancellation is expressed as r_(i)^((k))(t), Instep (a), calculating the said multi-path combining result of user i bya formula Y_(i)^((m)(k)) = Re{y_(i)^((m)(k))},  which can be expressedas Y_(i)^((m)(k)) = μ_(i)a_(i)^((m)) + n_(i),  where, n_(i) is aGaussian White Noise, which is subject to a normal distribution ofN(0, σ_(i)²), σ²  is a noise power of n_(i)a_(i)^((m))  is the mthsymbol of user i, whose value is +1 or −1, μ_(i) is a real number thatis related with channel fading; In step (b), expressing the said softdecision of the said multi-path combining result for user i asξ_(i)^((m)(k)) = f_(i)^((m)(k))â_(i)^((m)(k)),  calculating the saidsoft decision by a formula${{f_{i}^{{(m)}{(k)}}{\hat{a}}_{i}^{{(m)}{(k)}}} = {\tanh\left\{ {w\frac{\mu_{i}Y_{i}^{{(m)}{(k)}}}{\sigma_{i}^{2}}} \right\}}},$ where, W is a positive real number, â_(i)^((m)(k))  is a soft decisionresult for the mth symbol of user i, f_(i)^((m)(k))  is a reliabilitycoefficient of â_(i)^((m)(k)); Assuming the said weight value of thesaid soft decision in the kth stage is p^((k)), and then in step (c)calculating the said weighted soft decision of user i in symbol level bya formula ρ_(i)^((m)(k)) = ζ_(i)^((m)(k)) ⋅ p^((k)); In step (d),calculating the said weighed regenerated signal in chip level of user iby a formula${{g_{i}^{(k)}(t)} = {\sum\limits_{l = 1}^{L}\;{A_{il}{\sum\limits_{n = {- \infty}}^{\infty}\;{\rho_{i}^{{(n)}{(k)}}{\hat{a}}_{i}^{{(n)}{(k)}}{p\left( {t - {nT}_{b} - \tau_{il}} \right)}{c_{i}\left( {t - \tau_{il}} \right)}}}}}},$ where, A_(il) is a estimation value of a_(il)√{square root over(P_(i))}, a_(il) refers to a channel fading value of user i in the pathl, and p_(i) is a power of user i; In step (e), calculating the saidmultiple access interference on user i by a formula${{\hat{I}}_{i}^{(k)} = {\sum\limits_{{j = 1},{j \neq i}}^{N}\;{g_{j}^{(k)}(t)}}};$In step (f), calculating the said output signal of user i by a formular_(i)^((k + 1))(t) = r(t) − Î_(i)^((k)),  where, r(t) is the said baseband signal of the said received signal.
 8. The double weightingparallel interference cancellation method according to claim 7, whereinstep (b) of making the said soft decision of the said multi-pathcombining result for user i comprises, replacing the said Hyperbolictangent decision with a piecewise linear decision, which meansapproaching a Hyperbolic tangent function tanh(x) with a piecewiselinear decision function L(x), a expression of the piecewise lineardecision function L(x) is: ${L(x)} = \left\{ {\begin{matrix}{1,{x > \theta}} \\{{L_{q}(x)},{x \in \left\lbrack {x_{q - 1},x_{q}} \right\rbrack}} \\{{- {L_{q}\left( {- x} \right)}},{x \in \left\lbrack {{- x_{q}},{- x_{q - 1}}} \right\rbrack}} \\{{- 1},{x < {- \theta}}}\end{matrix}.} \right.$
 9. The double weighting parallel interferencecancellation method according to claim 7, wherein step (b) of making thesaid soft decision of the said multi-path combining result for user icomprises, replacing the said Hyperbolic tangent decision with a look-uptable method, which means approaching a Hyperbolic tangent functiontanh(x) with a decision function T(x) of the look-up table method, aexpression of the said decision function T(x) of the said look-up tablemethod is: ${T(x)} = \left\{ {\begin{matrix}{1,{x > \theta}} \\{{\tanh\left( \frac{x_{q - 1} + x_{q}}{2} \right)},{x \in \left\lbrack {x_{q - 1},x_{q}} \right\rbrack}} \\{{- {\tanh\left( \frac{x_{q - 1} + x_{q}}{2} \right)}},{x \in \left\lbrack {{- x_{q}},x_{q - 1}} \right\rbrack}} \\{{- 1},{x < \theta}}\end{matrix}.} \right.$
 10. The double weighting parallel interferencecancellation method according to claim 6, wherein step (c) comprises,setting the different weight value p^((k)) for the soft decision indifferent stages, wherein p⁽¹⁾<p⁽²⁾ . . . <p^((k)) . . . <p^((S)),where, k is the kth stage of the interference cancellation, and S is astage number of the interference cancellation.